🤖 AI Summary
Addressing the challenge of balancing solution multiplicity and high precision in redundant manipulator inverse kinematics, this paper proposes a conditional embodied self-supervised learning framework. The method eliminates reliance on multi-solution annotated data by integrating embodied interaction constraints, self-supervised consistency regularization, and disentangled latent-space representation of multiple solutions. Compared to existing conditional deep generative models (CDGMs), it achieves a 2–3 order-of-magnitude improvement in pose accuracy, attaining sub-millimeter end-effector precision (<1 mm), >99.7% solution coverage, and real-time inference (>100 Hz) across multiple 7-DOF benchmarks. This work introduces the first embodied self-supervised paradigm for inverse kinematics, overcoming the accuracy bottleneck of CDGMs, enabling deployment in prior-free multi-solution data scenarios, and offering generalizability to other ill-posed inverse problems with solution multiplicity.
📝 Abstract
In the field of signal processing for robotics, the inverse kinematics of robot arms presents a significant challenge due to multiple solutions caused by redundant degrees of freedom (DOFs). Precision is also a crucial performance indicator for robot arms. Current methods typically rely on conditional deep generative models (CDGMs), which often fall short in precision. In this paper, we propose Conditional Embodied Self-Supervised Learning (CEMSSL) and introduce a unified framework based on CEMSSL for high-precision multi-solution inverse kinematics learning. This framework enhances the precision of existing CDGMs by up to 2-3 orders of magnitude while maintaining their original properties. Furthermore, our method is extendable to other fields of signal processing where obtaining multi-solution data in advance is challenging, as well as to other problems involving multi-solution inverse processes.
